app.py

import gradio as gr
import requests
import json
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
import os

def get_api():
    api_key = os.getenv("NYT_ARTICLE_API")
    if api_key is None:
        raise ValueError("NYT_ARTICLE_API environment variable not set.")
    return api_key

def get_abstracts(query):
    api_key = get_api()
    url = f'https://api.nytimes.com/svc/search/v2/articlesearch.json?q={query}&fq=source:("The New York Times")&api-key={api_key}'
    response = requests.get(url).json()
    abstracts = []
    docs = response.get('response', {}).get('docs', [])
    for doc in docs:
        abstract = doc.get('abstract', '')
        if abstract:
            abstracts.append(abstract)
    return abstracts

def summarizer(query):
    abstracts = get_abstracts(query)
    input_text = ' '.join(abstracts)
    
    tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_billsum_model")
    inputs = tokenizer(input_text, return_tensors="tf").input_ids
    
    model = TFAutoModelForSeq2SeqLM.from_pretrained("stevhliu/my_awesome_billsum_model", from_pt=True)
    outputs = model.generate(inputs, max_length=100, do_sample=False)
    
    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return abstracts, summary

iface = gr.Interface(
    fn=summarizer,
    inputs=gr.inputs.Textbox(placeholder="Enter your query"),
    outputs=[
        gr.outputs.Textbox(label="Abstracts"),
        gr.outputs.Textbox(label="Summary")
    ],
    title="New York Times Articles Summarizer",
    description="This summarizer actually does not yet summarize New York Times articles because of certain limitations. Type in something like 'Manipur' or 'Novak Djokovic' you will get a summary of that topic. What actually happens is that the query goes through the API. The abstract of article's content is added or concatenated, and then a text of considerable length is generated. That text is then summarized. So, this is an article summarizer but summarizes only abstracts of a particular article, ensuring that readers get the essence of a topic. This is a successful implementation of a pretrained T5 Transformer model."
)

if __name__ == "__main__":
    iface.launch()